Our videos are a step-by-step journey across the fundamental topics in Artificial Intelligence and Machine Learning. Each video has a corresponding tutorial and interactive code-demo, which are important tools for learning how to apply the concepts explained.

This is the first video in the series. This video covers the essential background for understanding supervised learning, regression, and classification.

**Difficulty - Beginner**

**Keywords - Regression, Classification, Models**

This is the second video in the series. This video goes over linear models, loss, hyperparameters, stochastic and batch gradient descent, multidimensional gradient descent, etc.

**Difficulty - Intermediate**

**Keywords - Linear Regression, Loss, Gradient Descent**

This is the third video in the series. This video goes over Scikit-Learn, NumPy, SciPy, ML Libraries, Coding in Sklearn, data visualization

**Difficulty - Intermediate**

**Keywords - Scikit-Learn, NumPy, SciPy**

This is the fourth video in the series. This video goes over Classifiers, Decision Trees, KNN, Artificial Neural Nets, Naive Bayes, Lazy/Eager Learners, Building a Decision Tree, Overfitting, Underfitting, Pruning a Decision Tree, Coding a DT in Scikit-learn

**Difficulty - Beginner**

**Keywords - Decision Tree, Pruning, Fitting**

This is the fifth video in the series. This video goes over Logistic Regression, Sigmoid Function, Decision Boundaries, Probability boundaries, Cross-Entropy Loss, Support Vector Machines, Hyperplane, Margin, Kernel Transformations, Hinge Loss, Gamma Parameters, Regularization Tuning

**Difficulty - Advanced**

**Keywords - Support Vector Machine, Hyperplane, Kernels**

This is the sixth video in the series. This video goes over limitations of logistic regression, linear discriminant analysis, learning LDA, dimensionality reduction, projection into lower dimension spaces, overfitting with higher dimensional data, Principal Components Analysis, PCA example, and visualization

**Difficulty - Advanced**

**Keywords - PCA, Linear Discriminant Analysis, Dimensions**

This is the seventh video in the series. This video goes over Bayesian Machine Learning, Conditional Probability, Bayes Theorem, Multivariate distributions, Prior Probability, Posterior Probability, Gaussian PDF, Machine Learning Pipelines, chaining of an ML pipeline

**Difficulty - Intermediate**

**Keywords - Bayesian ML, Probability, Pipelines**

This is the eight video in the series. This video goes over Random Forest Classifiers, Advantages, Disadvantages, K-nearest neighbors classifier, choosing "K", balancing error, sci-kit learn implementation

**Difficulty - Intermediate**

**Keywords - Random Forest, K-nearest Neighbors**

This is the ninth video in the series. This video goes over goes over graph algorithms, reflex v. state models, search trees, nodes/edge cost, MCP, graph traversal, backtracking, BFS, DFS, Dynamic Programming, State graphs, Uniform Cost Search (UCS), Space and Time Complexity

**Difficulty - Advanced**

**Keywords - Searching Algorithms, Higher Logic ML**